Continuous Data Validation Using AI ML-Driven Statistical Profiling in Bronze–Silver–Gold Architecture

Abstract

: In modern data-intensive enterprises, the correctness and reliability of data have become as critical as the scalability of data processing systems themselves. Organizations increasingly rely on large-scale analytical platforms, artificial intelligence models, and real-time decision systems that ingest data continuously from heterogeneous sources. However, the growing velocity, volume, and variety of data significantly increase the risk of data-quality degradation. Traditional rule-based data validation approaches, which depend on static thresholds and manually defined constraints, struggle to adapt to evolving data patterns, schema changes, and non-stationary distributions. As a result, data quality issues often remain undetected until they impact downstream analytics, machine learning models, or business decisions. This research proposes a comprehensive framework for continuous data validation using Artificial Intelligence and Machine Learning–driven statistical profiling within the Bronze–Silver–Gold architecture. The proposed approach embeds intelligent validation mechanisms directly into each architectural layer, enabling early detection of anomalies, schema drift, distribution shifts, and semantic inconsistencies. Unlike point-in-time or batch-based validation techniques, the framework continuously learns baseline statistical characteristics of data attributes, including central tendency, dispersion, frequency distributions, cardinality, null ratios, and temporal behavior. Incoming data is evaluated against these learned profiles using adaptive, data-driven thresholds rather than rigid predefined rules. At the Bronze layer, raw ingested data is statistically profiled to establish source-level behavioral baselines while preserving original fidelity. The Silver layer applies refined validation on standardized data, leveraging machine learning–based drift detection and anomaly identification to ensure consistency and integrity. The Gold layer focuses on business-level validation, where aggregated metrics and key performance indicators are continuously monitored using time-series and regression-based models to ensure analytical trustworthiness. A closed feedback loop enables continuous learning, allowing validation models to evolve alongside changing data ecosystems. A large-scale enterprise case study demonstrates that the proposed framework significantly improves anomaly detection accuracy, reduces false-positive rates, shortens detection latency, and lowers manual intervention. By combining architectural design principles with AI-driven statistical profiling, this research establishes a robust, scalable, and autonomous foundation for trustworthy data platforms suitable for professional conference-level data engineering and artificial intelligence systems.